ArticleSleep & breathing = Schlaf & Atmung2026
Predicting positive airway pressure appointment attendance using machine learning and deep learning: the role of clinical and oximetric indicators.
Article in Sleep & breathing = Schlaf & Atmung, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionParticipation in positive airway pressure (PAP) therapy is essential for improving long-term health outcomes in individuals diagnosed with obstructive sleep apnea syndrome (OSAS). This study aimed to identify clinical, physiological, and behavioral factors influencing attendance at initial PAP appointments and to develop and compare predictive models using machine learning and deep learning techniques.
methodsA total of 369 patients with obstructive sleep apnea syndrome (OSAS) were retrospectively analyzed. Among them, 39.2% attended their scheduled PAP therapy appointment and initiated treatment, whereas 60.8% did not. Correlation analyses were performed, and predictive models including logistic regression, decision trees, random forests, support vector machines, and a deep neural network were developed and compared to predict appointment attendance.
resultsA high Epworth Sleepiness Scale (ESS) score and greater time spent in the 80–90% nocturnal oxygen saturation range were positively associated with PAP therapy attendance. In contrast, alcohol consumption was a significant behavioral predictor of non-attendance. Hypoxemia-related metrics, such as average nocturnal oxygen saturation, were found to be stronger predictors than the apnea–hypopnea index (AHI). Anthropometric variables—waist circumference, body mass index (BMI), and neck circumference—showed only weak positive correlations with treatment participation.
conclusionThis study is among the few to focus on initiation of PAP therapy, rather than long-term adherence, which is the typical focus of most existing research. The machine learning and deep learning models developed here have the potential to support clinical decision-making systems by identifying high-risk individuals early and enabling timely intervention. Integrating these tools into clinical workflows may improve patient engagement and outcomes from the very first step of treatment.
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